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检索条件"主题词=deep learning in robotics and automation"
221 条 记 录,以下是61-70 订阅
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Use of deep learning for Characterization of Microfluidic Soft Sensors
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IEEE robotics AND automation LETTERS 2018年 第2期3卷 873-880页
作者: Han, Seunghyun Kim, Taekyoung Kim, Dooyoung Park, Yong-Lae Jo, Sungho Korea Adv Inst Sci & Technol Sch Comp Daejeon 305701 South Korea Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Inst Adv Machines & Design Seoul 08826 South Korea
Soft sensors made of highly deformable materials are one of the enabling technologies to various soft robotic systems, such as soft mobile robots, soft wearable robots, and soft grippers. However, major drawbacks of s... 详细信息
来源: 评论
SOZIL: Self-Optimal Zero-Shot Imitation learning
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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2023年 第4期15卷 2077-2088页
作者: Hao, Peng Lu, Tao Cui, Shaowei Wei, Junhang Cai, Yinghao Wang, Shuo Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China
Zero-shot imitation learning has demonstrated its superiority to learn complex robotic tasks with less human participation. Recent studies show convincing performance under the condition that the robot follows the dem... 详细信息
来源: 评论
learning Long-Range Perception Using Self-Supervision From Short-Range Sensors and Odometry
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IEEE robotics AND automation LETTERS 2019年 第2期4卷 1279-1286页
作者: Nava, Mirko Guzzi, Jerome Chavez-Garcia, R. Omar Gambardella, Luca M. Giusti, Alessandro USI SUPSI Dalle Molle Inst Artificial Intelligence IDSIA CH-6928 Lugano Switzerland
We introduce a general self-supervised approach to predict the future outputs of a short-range sensor (such as a proximity sensor) given the current outputs of a long-range sensor (such as a camera). We assume that th... 详细信息
来源: 评论
Interactive Gibson Benchmark: A Benchmark for Interactive Navigation in Cluttered Environments
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 713-720页
作者: Xia, Fei Shen, William B. Li, Chengshu Kasimbeg, Priya Tchapmi, Micael Edmond Toshev, Alexander Martin-Martin, Roberto Savarese, Silvio Stanford Univ SAIL Stanford CA 94305 USA Google Robot Mountain View CA 94043 USA
We present Interactive Gibson Benchmark, the first comprehensive benchmark for training and evaluating Interactive Navigation solutions. Interactive Navigation tasks are robot navigation problems where physical intera... 详细信息
来源: 评论
Distributed Proprioception of 3D Configuration in Soft, Sensorized Robots via deep learning
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 3299-3306页
作者: Truby, Ryan L. Della Santina, Cosimo Rus, Daniela MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA
Creating soft robots with sophisticated, autonomous capabilities requires these systems to possess reliable, on-line proprioception of 3D configuration through integrated soft sensors. We present a framework for predi... 详细信息
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Fast and Continuous Foothold Adaptation for Dynamic Locomotion Through CNNs
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IEEE robotics AND automation LETTERS 2019年 第2期4卷 2140-2147页
作者: Magana, Octavio Antonio Villarreal Barasuol, Victor Camurri, Marco Franceschi, Luca Focchi, Michele Pontil, Massimiliano Caldwell, Darwin G. Semini, Claudio Ist Italiano Tecnol Dynam Legged Syst Lab I-16163 Genoa Italy Univ Oxford Oxford Robot Inst Oxford OX2 6NN England Ist Italian Tecnol Computat Stat & Machine Learning I-16163 Genoa Italy Ist Italiano Tecnol Dept Adv Robot I-16163 Genoa Italy
Legged robots can outperform wheeled machines for most navigation tasks across unknown and rough terrains. For such tasks, visual feedback is a fundamental asset to provide robots with terrain awareness. However, robu... 详细信息
来源: 评论
Scene Compliant Trajectory Forecast With Agent-Centric Spatio-Temporal Grids
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 2816-2823页
作者: Ridel, Daniela Deo, Nachiket Wolf, Denis Trivedi, Mohan Univ Sao Paulo Mobile Robot Lab BR-13566 Sao Carlos SP Brazil Univ Calif San Diego Lab Intelligent & Safe Automobiles San Diego CA 92092 USA
Forecasting long-term human motion is a challenging task due to the non-linearity, multi-modality and inherent uncertainty in future trajectories. The underlying scene and past motion of agents can provide useful cues... 详细信息
来源: 评论
learning Fast Adaptation With Meta Strategy Optimization
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IEEE robotics AND automation LETTERS 2020年 第2期5卷 2950-2957页
作者: Yu, Wenhao Tan, Jie Bai, Yunfei Coumans, Erwin Ha, Sehoon Google Robot Mountain View CA 94043 USA Georgia Inst Technol Atlanta GA 30332 USA
The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-learning algorithm for training polici... 详细信息
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Inference Over Distribution of Posterior Class Probabilities for Reliable Bayesian Classification and Object-Level Perception
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IEEE robotics AND automation LETTERS 2018年 第4期3卷 4329-4336页
作者: Tchuiev, Vladimir Indelman, Vadim Technion Israel Inst Technol Dept Aerosp Engn IL-32000 Haifa Israel
State of the art Bayesian classification approaches typically maintain a posterior distribution over possible classes given available sensor observations (images). Yet, while these approaches fuse all classifier outpu... 详细信息
来源: 评论
Real-Time 3-D Shape Instantiation for Partially Deployed Stent Segments From a Single 2-D Fluoroscopic Image in Fenestrated Endovascular Aortic Repair
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IEEE robotics AND automation LETTERS 2019年 第4期4卷 3703-3710页
作者: Zheng, Jian-Qing Zhou, Xiao-Yun Riga, Celia Yang, Guang-Zhong Imperial Coll London Hamlyn Ctr Robot Surg London SW7 2AZ England St Marys Hosp Reg Vasc Unit London W2 1NY England Imperial Coll London Acad Div Surg London SW7 2A2 England
In fenestrated endovascular aortic repair (FEVAR), accurate alignment of stent graft fenestrations or scallops with aortic branches is essential for establishing complete blood flow perfusion. Current navigation is la... 详细信息
来源: 评论